Instructions to use zai-org/cogvlm-chat-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/cogvlm-chat-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/cogvlm-chat-hf", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zai-org/cogvlm-chat-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zai-org/cogvlm-chat-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/cogvlm-chat-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/cogvlm-chat-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/cogvlm-chat-hf
- SGLang
How to use zai-org/cogvlm-chat-hf with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zai-org/cogvlm-chat-hf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/cogvlm-chat-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zai-org/cogvlm-chat-hf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/cogvlm-chat-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zai-org/cogvlm-chat-hf with Docker Model Runner:
docker model run hf.co/zai-org/cogvlm-chat-hf
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README.md
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# 4</s>
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```
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当单卡显存不足时,可以将模型切分到多个小显存GPU上
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dispatch the model into multiple GPUs with smaller VRAM.
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```python
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import torch
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# 4</s>
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```
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当单卡显存不足时,可以将模型切分到多个小显存GPU上。以下是个当你有两张24GB的GPU,16GBCPU内存的例子。
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你可以将`infer_auto_device_map`的参数改成你的配置。注意这里将GPU显存少写了一点,这是为推理时中间状态预留出一部分显存。
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dispatch the model into multiple GPUs with smaller VRAM. This is an example for you have two 24GB GPU and 16GB CPU memory.
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you can change the arguments of `infer_auto_device_map` with your own setting.
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```python
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import torch
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